Multiple negative selection algorithm: Improving detection error rates in IoT intrusion detection systems

Pamukov, Marin E.; Poulkov, Vladimir

doi:10.1109/idaacs.2017.8095140

Cited by 20 publications

(16 citation statements)

References 6 publications

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“…One of the shortcomings of this method is that it has not been validated in an actual implementation. Furthermore, the authors in [51] proposed the MNSA algorithm, which is a combination of negative selection and positive selection detectors. The first set of detectors can recognize self-data, and the other set of detectors is used to detect non-self-data.…”

Section: Negative and Positive Algorithmsmentioning

confidence: 99%

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Alrubayyi

Goteng

Jaber

et al. 2021

JSAN

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The fast growth of the Internet of Things (IoT) and its diverse applications increase the risk of cyberattacks, one type of which is malware attacks. Due to the IoT devices’ different capabilities and the dynamic and ever-evolving environment, applying complex security measures is challenging, and applying only basic security standards is risky. Artificial Immune Systems (AIS) are intrusion-detecting algorithms inspired by the human body’s adaptive immune system techniques. Most of these algorithms imitate the human’s body B-cell and T-cell defensive mechanisms. They are lightweight, adaptive, and able to detect malware attacks without prior knowledge. In this work, we review the recent advances in employing AIS for the improved detection of malware in IoT networks. We present a critical analysis that highlights the limitations of the state-of-the-art in AIS research and offer insights into promising new research directions.

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Section: Negative and Positive Algorithmsmentioning

confidence: 99%

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Alrubayyi

Goteng

Jaber

et al. 2021

JSAN

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“…Pamukov and V.K. Poulkov [56] proposed a new model for IDS in IoT networks based on the Negative Selection Algorithm (NSA), and they called it Multiple Negative Selection Algorithm (MNSA). The main objective of this proposal is to reduce detection errors without the need for human interventions.…”

Section: Ids-based Security Solution In Iotmentioning

confidence: 99%

“…The rate of successful detection of attacks in their IDS was 90%. Table 4 presents the comparison between the papers related to IDS-based security solutions in IoT [52,55,56,62,63,[66][67][68][69].…”

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confidence: 99%

MADS Based on DL Techniques on the Internet of Things (IoT): Survey

Talal

Zagrouba

2021

Electronics

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Technologically speaking, humanity lives in an age of evolution, prosperity, and great development, as a new generation of the Internet has emerged; it is the Internet of Things (IoT) which controls all aspects of lives, from the different devices of the home to the large industries. Despite the tremendous benefits offered by IoT, still there are some challenges regarding privacy and information security. The traditional techniques used in Malware Anomaly Detection Systems (MADS) could not give us as robust protection as we need in IoT environments. Therefore, it needed to be replaced with Deep Learning (DL) techniques to improve the MADS and provide the intelligence solutions to protect against malware, attacks, and intrusions, in order to preserve the privacy of users and increase their confidence in and dependence on IoT systems. This research presents a comprehensive study on security solutions in IoT applications, Intrusion Detection Systems (IDS), Malware Detection Systems (MDS), and the role of artificial intelligent (AI) in improving security in IoT.

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“…An IoT IDS was proposed by Marin E Pamukov [19] to improve the detection rate and performance of the detection system in IoT devices. The authors use multiple negative selection algorithms to reduce the errors in intrusion detection and it can runs without input operators.…”

Section: Literature Surveymentioning

confidence: 99%

Malicious Traffic classification using Long Short-Term Memory (LSTM) model

Thapa

Duraipandian²

2021

Preprint

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Malicious traffic classification is the initial and primary step for any network-based security systems. This traffic classification systems include behavior-based anomaly detection system and Intrusion Detection System. Existing methods always relies on the conventional techniques and process the data in the fixed sequence, which may leads to performance issues. Furthermore, conventional techniques require proper annotation to process the volumetric data. Relying on the data annotation for efficient traffic classification may leads to network loops and bandwidth issues within the network. To address the above-mentioned issues, this paper presents a novel solution based on artificial intelligence perspective. The key idea of this paper is to propose a novel malicious classification system using Long Short-Term Memory (LSTM) model. To validate the efficiency of the proposed model, an experimental setup along with experimental validation is carried out. From the experimental results, it is proven that the proposed model is better in terms of accuracy, throughput when compared to the state-of-the-art models. Further, the accuracy of the proposed model outperforms the existing state of the art models with increase in 5% and overall 99.5% in accuracy.

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Multiple negative selection algorithm: Improving detection error rates in IoT intrusion detection systems

Cited by 20 publications

References 6 publications

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

Challenges of Malware Detection in the IoT and a Review of Artificial Immune System Approaches

MADS Based on DL Techniques on the Internet of Things (IoT): Survey

Malicious Traffic classification using Long Short-Term Memory (LSTM) model

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